Foundation models have achieved remarkable success across various domains due to their ability to learn generalizable representations from large-scale, unlabeled datasets. In the geospatial domain, several foundation models have been developed to leverage the abundance of unlabeled remote sensing data and support Earth observation tasks across diverse regions and sensor types. However, the geolocation-dependent characteristics of remote sensing data introduce unique challenges in adapting these models to region-focused applications. By conducting a comprehensive empirical analysis across diverse geographical regions and tasks, we explore whether incorporating regional information during pretraining or fine-tuning improves performance on region-specific downstream tasks. We show that regional representation learning, as well as regional adaptation of features extracted from a globally trained foundation model, is beneficial when the region-specific performance of the downstream tasks is of interest. To this end, we also propose a regional adaptation to the globally trained foundation models to balance global diversity with regional representation learning for improved performance.